Motivation and the careers of agricultural entrepreneurs in Ivory Coast
Bibliographic record
Abstract
Purpose Following an incursion into the business world as an entrepreneur, many people go back to being salaried workers or unemployed. Using self-determination theory (SDT), this study aims to test the effects of self-determined motivation of Ivorian agricultural entrepreneurs on career retention, career satisfaction and subjective well-being. Design/methodology/approach The sample under study consisted of 171 agricultural entrepreneurs in the Ivory Coast who owned their businesses. Research hypotheses were tested using structural equation modelling. Findings The findings reveal that the degree of self-determination in the motivation of Ivorian agricultural entrepreneurs does not have a direct effect on their intention as to whether or not to remain an entrepreneur. The findings nevertheless show that the more the degree of motivation of Ivorian agricultural entrepreneurs is self-determined, the greater satisfaction they experience with their entrepreneurial career and the higher their subjective well-being. The findings also show that satisfaction with the entrepreneurial career is a predictor of career retention among Ivorian agricultural entrepreneurs. In addition, career retention and subjective well-being are reciprocally linked, as are subjective well-being and career satisfaction. Practical implications Career retention of agricultural entrepreneurs in the Ivory Coast can be achieved through career satisfaction and self-determined motivation. Originality/value Based on previous studies on SDT in an organizational context, this study is, to the best of the knowledge, the first to have tested a theoretical model that can explain career retention, career satisfaction and subjective well-being among entrepreneurs. This research brings to light these processes among agricultural entrepreneurs in Ivory Coast.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".